Neural networks in high-performance liquid chromatography optimization:Response surface modeling

The usefulness of artificial neural networks for response surface modeling in HPLC optimization is compared with (non-)linear regression methods. The number of hidden nodes is optimized by a lateral inhibition method. Overfitting is controlled by cross-validation using the leave one out method (LOOM). Data sets of linear and non-linear response surfaces (capacity factors) were taken from literature. The results show that neural networks offer promising possibilities in HPLC method development. The predictive results were better or comparable to those obtained with linear and non-linear regression models

Simultaneous resolution of overlapping peaks in high-performance liquid chromatography and micellar electrokinetic chromatography with diode array detection using augmented iterative target transformation factor analysis

In this paper, augmentation has been applied to data matrices, which originate from hyphenated methods that share the same mode of detection, but use different separation methods, HPLC-DAD and MEKC-DAD. A novel method, wavelength shift eigenstructure tracking (WET), has been proposed for the alignment between the wavelength scale of both detectors. WET proves to be suitable for the detection as well as correction of wavelength shift between both detectors. After correction of the wavelength scale, data obtained on both systems have been augmented and submitted to iterative target transformation factor analysis. Augmented curve resolution provides significantly better estimates of the chromatographic and electrophoretic profiles and spectra than the use of non-augmented curve resolution on HPLC and MEKC data separately. It is particularly useful when the pure fraction of a chromatographic peak is less than 0.10. Finally, the relative weight of MEKC versus HPLC in augmentation may be increased using intensity and noise normalisation. However, since noise normalisation and its accompanying decrease in signal-to-noise ratio leads to a loss of information, and, since intensity normalisation may cause a failure of the augmented curve resolution algorithm, benefits and drawbacks of normalisation should be weighed on a case-by-case basis. (c) 2005 Elsevier B.V. All rights reserved

Comparison of prediction power between theoretical and neural-network models in ion-interaction chromatography

The separation by ion-interaction chromatography (IIC) of metal complexes having single and double charges has been studied in order to compare the prediction power of oft (neural-network) and hard modelling (IIC equation). The two approaches have been used to model the retention behaviour as a function of the composition of the mobile phase, With ion-interaction mobile phases, the parameters involved included the concentrations of ion-interaction reagent, organic modifier and ionic strength. From a set of 69 experimental design points (the different mobile phase compositions at which capacity factors are measured), one test set of ten design points and ten training sets, containing from 59 to 11 design points, have been extracted. Chromatographic and chemometric considerations for the selection of the data sets and minimum number of observations required have been discussed. The study showed that the IIC equation predicted more accurately when few experimental data were available, while a similar prediction power was obtained with both models when the number of data was more than 17. Nevertheless the neural-network accounted for a greater versatility without the need to develop an equation. (C) 1998 Elsevier Science B.V

Multivariate characterization of solvent strength and solvent selectivity in reversed-phase high-performance liquid chromatography

Principal component analysis was used to determine the dimensionality and structure of three data sets consisting of the capacity factors of eleven to twenty different solutes measured in nine different mobile phase compositions consisting of water and methanol and/or acetonitrile on three reversed-phase columns. Principal component analysis showed that two principal components could account for the total variance in the data and that the percentage variance explained by the first principal component (about 80-95%) was much greater than the percentage explained by the second principal component, but that the percentage depended strongly on the choice of solutes for the sample. The first principal component could be associated with solvent strength and solvent strength selectivity and the second principal component with modifier selectivity. Solutes that showed strong modifier selectivity could be distinguished from solutes that have almost zero modifier selectivity, which could be useful for the definition of an empirical solvent strength scale